Classification Metrics

• Accuracy

Accuracy is perhaps the most intuitive classification metric. It measures the ratio of correctly predicted instances to the total number of instances in the dataset. However, accuracy can be misleading, especially when dealing with imbalanced datasets. In such cases, a model that predicts the majority class most of the time can still achieve high accuracy, even if it fails to correctly predict minority class instances.

• Precision and Recall

Precision and recall are important metrics for imbalanced datasets and are often used together.

• Precision : Precision measures the accuracy of positive predictions. It’s the ratio of true positives (correctly predicted positive instances) to the total number of positive predictions. High precision indicates that when the model predicts a positive class, it’s likely to be correct.

• Recall : Recall, also known as sensitivity or true positive rate, measures the ability of the model to identify all relevant instances of the positive class. It’s the ratio of true positives to the total number of actual positive instances. High recall indicates that the model can successfully identify most of the positive instances.

• F1-Score

The F1-score is the harmonic mean of precision and recall. It provides a single score that balances both precision and recall. The F1-score is particularly useful when you want to find a balance between false positives and false negatives. It’s calculated as:

• Area Under the Receiver Operating Characteristic Curve (AUC-ROC)

ROC (Receiver Operating Characteristic) curves are useful for binary classification problems. They plot the true positive rate (recall) against the false positive rate at various thresholds. The AUC-ROC measures the area under the ROC curve and provides a single value that summarizes the model’s ability to distinguish between classes. A higher AUC-ROC indicates better discrimination.

• Area Under the Precision-Recall Curve (AUC-PR)

Similar to AUC-ROC, the AUC-PR measures the area under the precision-recall curve. It is particularly useful when dealing with imbalanced datasets where the positive class is rare. A higher AUC-PR indicates better precision-recall trade-off.

LightGBM (Light Gradient Boosting Machine) is a popular gradient boosting framework developed by Microsoft known for its speed and efficiency in training large datasets. It’s widely used for various machine-learning tasks, including classification, regression, and ranking. While training a LightGBM model is relatively straightforward, evaluating its performance is just as crucial to ensuring its effectiveness in real-world applications.

In this article, we will explore the key evaluation metrics used to assess the performance of LightGBM models.